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The equity and spatial implications of transit fare

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  • Zhou, Jiangping
  • Zhang, Min
  • Zhu, Pengyu

Abstract

Availability of new open/big data (NOBD) such as smartcard and General Transit Feed Specification data has provided unprecedented opportunities for transit planners and policy-analysts to conduct analyses that are highly challenging and even infeasible where only traditional data (e.g., censuses/surveys) are in presence. In this study, we first review and summarize discrete and scattering existing studies on (a) society and justice, (b) transportation/space and justice, and (c) transit fare and justice. We consider (c) as a subset of (b) and (b) as a subset of (a). We then illustrate how NOBD can supplement traditional data in the studies of the equity and spatial implications of transit fares via an exploratory study of Brisbane, Australia. Specifically, we propose and implement methods or procedures such as “trajectory rebuilding”, “fare matching”, “segment tagging”, “desired line/stop visualisation”, “commuter identification” and “scenario analysis” to show why and how transit fares could have important equity and spatial implications. In addition to empirical findings and policy recommendations, we offer some transferable methods and procedures for visualising and concretizing the aforementioned implications.

Suggested Citation

  • Zhou, Jiangping & Zhang, Min & Zhu, Pengyu, 2019. "The equity and spatial implications of transit fare," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 309-324.
    • Handle: RePEc:eee:transa:v:121:y:2019:i:c:p:309-324
    DOI: 10.1016/j.tra.2019.01.015
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    Cited by:

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    2. Zhan, Shuguang & Wong, S.C. & Lo, S.M., 2020. "Social equity-based timetabling and ticket pricing for high-speed railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 165-186.
    3. Guo, Qianwen & Sun, Yanshuo & Schonfeld, Paul & Li, Zhongfei, 2021. "Time-dependent transit fare optimization with elastic and spatially distributed demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 353-378.
    4. Kębłowski, Wojciech & Dobruszkes, Frédéric & Boussauw, Kobe, 2022. "Moving past sustainable transport studies: Towards a critical perspective on urban transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 74-83.
    5. Zhu, Pengyu & Zhao, Songnian & Jiang, Yanpeng, 2022. "Residential segregation, built environment and commuting outcomes: Experience from contemporary China," Transport Policy, Elsevier, vol. 116(C), pages 269-277.
    6. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    7. Wang, Siqin & Liu, Yan & Corcoran, Jonathan, 2021. "Equity of public transport costs before and after a fare policy reform: An empirical evaluation using smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 104-118.
    8. Jomehpour Chahar Aman, Javad & Smith-Colin, Janille, 2020. "Transit Deserts: Equity analysis of public transit accessibility," Journal of Transport Geography, Elsevier, vol. 89(C).

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